IEEE MMTC Image, Video and Mesh coding Interest Group The Image, Video and Mesh Coding (IMVIMEC) Interest Group provides a forum for discussion on a variety of IMVIMEC related activities and topics. In particular, this Interest Group will introduce and explore past and current trends in Image/Video/Mesh coding and standardization, determine areas of improvement and relationships with multimedia communications, and potentially suggest and contribute to new directions for image, video and mesh coding with a focus on new/emerging applications and…

What press say about us!

Can AI change the game for fake news?
The Institute of Current World Affairs (ICWA) has published an article, written by Amanda Mikesell, regarding fake news. The article features the FANDANGO Project, the goal of which is to help journalists in the verification and fact-checking process of news pieces.
Read the full article here.

Set-and-forget surveillance cameras wake themselves up and call for help
Despite their advantages, digital security cameras are inconvenient. A new surveillance model reports on crime by itself.
Digital surveillance cameras have come a long way in a short time. They now combine very-high–quality images with autonomous operation and convenient sizes. Therefore, such devices are widely used in law enforcement.
Nevertheless, video surveillance remains costly, power hungry and complicated. Current systems are used mainly for recording and have very little capability for real-time scene interpretation.
The EU-funded FORENSOR project has developed a new sensor system that addresses these problems. The sensor is small and concealable; its low-power requirements free it from electrical infrastructure. The device can operate for months. Hence, the sensor is also capable of a degree of independent operation.
You can read the full article here.

AI system finds and predicts criminal patterns in vast troves of surveillance footage
Security organisations can’t keep up with the sheer volume of surveillance footage available today. The machine-learning based SURVANT system sifts through the data to find patterns of crimes and even predicts their evolution.
Security organisations and agencies around the world deploy increasing amounts of video surveillance to monitor and protect people, property and public infrastructure. The amount of available footage is exploding, thanks to growing numbers of cameras operating at higher resolutions, making it difficult for human surveillance teams to analyze all of the footage – especially if they have other tasks.
Automated surveillance could help but requires advanced analytics to be effective in fighting crime. Many organizations have invested heavily in surveillance systems and are keen to exploit the video footage they have collected to this end. The EU-funded SURVANT project created a system to do exactly this.
“SURVANT addresses system scalability issues that emerge from the explosion in the amount of available video content,” says Mr Giuseppe Vella, SURVANT project coordinator. SURVANT analyses relevant surveillance videos to extract inter/intra-camera video analytics, before enriching this information with reasoning and inference. It then assists investigators to search efficiently and effectively through video archives, to find critical elements of criminal activity among the sea of footage.
You can read the full article here.

EU Commission has published success story about the Survant project.
An innovative artificial intelligence platform being developed by EU-funded researchers analyses CCTV and videos posted on social media to identify criminals, suspicious behaviour and events more quickly.
Researchers in the EU-funded SURVANT project are working on an innovative system that will be able to automatically gather and analyse videos and images. Their aim is to provide a user-friendly platform with advanced visualisation tools to help users search and examine footage, ultimately improving investigators’ capacity and efficiency.
‘SURVANT offers unique flexibility,’ explains the project’s technical director, Anastasios Dimou, of the Information Technologies Institute in Greece. ‘It supports a plethora of video and image formats and allows the investigator to build complex content-based queries with a user-friendly interface.’ Read the full article...

The MaTHiSiS project was featured on the popular Greek newspaper “TA NEA” and more specifically on the online version (www.tanea.gr) with 47183 unique visitors and 156920 page views during the day that the MaTHiSiS article was posted. The article “Robotics at the service of education” was posted on the column “My city” and received 162 likes and 260 shares, while the corresponding post on the newspaper’s Facebook page received 2068 unique users (the article is available only in Greek).
Date: Thursday, May 4, 2017
Links: Robotics at the service of education

The focus of the Visual Computing Laboratory is to develop new algorithms and architectures for applications in the areas of 3D processing, image/video processing, computer vision, pattern recognition, bioinformatics and medical imaging.